Handbook of Research
on Maximizing Cognitive
Learning through
Knowledge Visualization
Anna Ursyn
University of Northern Colorado, USA
A volume in the Advances in Knowledge
Acquisition, Transfer, and Management (AKATM)
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Handbook of research on maximizing cognitive learning through knowledge visualization / Anna Ursyn, editor.
pages cm
Includes bibliographical references and index.
ISBN 978-1-4666-8142-2 (hardcover) -- ISBN 978-1-4666-8143-9 (ebook) 1. Cognitive learning. 2. Information visualization. I. Ursyn, Anna, 1955- editor of compilation, author.
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494
Chapter 18
What Does Learning Look Like?
Data Visualization of Art
Teaching and Learning
Pamela G. Taylor
Virginia Commonwealth University, USA
ABSTRACT
Drawing upon the data visualization work of Lev Manovich and Manuel Lima, in this chapter the author discusses ways for envisioning and representing the complex teaching and learning that is associated with the visual arts. Experiences and examples are shared that use new and old technologies to
create and make connections among critically reflective collections of student learning artifacts such
as research, journals, preliminary sketches, work in other classes, and realms of experience outside
of school. Instead of relying on one final art product, the author explores embedded data mining and
visualization as a viable approach to gauging student learning. Following the lead education notables
Elliot Eisner (2002, 2004), John Dewey (1934), and Howard Gardner (1985), this research positions
the visual arts as a common thread throughout disciplines. Such inherent and fundamental visual arts
practices as portfolios, project-based instruction, and exhibition continue to expand instruction and
learning in such classes as English, math, science, and history. The implications include the possibility
that art education will lead the way to implementing authentic embedded assessment processes across
education disciplines and grade levels.
INTRODUCTION
Most every art teacher and artist knows that a
work of art is not simply one work of art. It is a
combination and progression of many and varied
ideas, influences, research, dreams, risks, and
other life and learning experiences too numerous
to mention. What if there were a way to critically
capture and actually represent this process of
learning and making art? How would it work and
what would it look like? These questions guide
and challenge this evolving research that actually
began in 1999.
Drawing upon experiences from a 2-year
computer hypertext-based high school art study1
(Taylor, 1999) and a 3-year beta software development study of a virtual 3-d linking environment2
(Taylor, 2014), this research is challenged and
DOI: 10.4018/978-1-4666-8142-2.ch018
Copyright © 2015, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

What Does Learning Look Like?
extended through data visualization theory and
practice. Specifically, this chapter will focus on
data visualization principles (Lima, 2011; Manovich, 2012) and design thinking methods/categories
(Berger, 2009) as possible approaches for mining
and representing new and old technological data
associated with teaching and learning in the visual
arts. Additionally, critical and valuable social aspects of data visualization theory will be shared
to represent as well as problematize sample visual
representations of learning.
UNDERSTANDING DATA
VISUALIZATION THROUGH
AN ARTIST’S EYE
Much like the typical pie charts and bar graphs
seen in math class and political analyses, data
visualization functions as a way to visually represent data sets. Supposedly, such visualizations
provide a clearer way to see and understand data
analyses than through a narrative explanation
alone. The idea of representing data in illustrative
ways actually dates back to Prehistoric art with
cave paintings, as well as to the carvings, scrolls,
story vases and petroglyphs of ancient Egyptians,
Greeks, Mayan, and Native Americans. Human beings have a strong need to see and understand data
visually. Be it to provide directions, explanations,
plans, strategies, or proof of an argument, more
often than not a pen, pencil, computer stylus or
touch pad is used to further an explanation. In the
case of such artists as Leonardo da Vinci, the data
visualized and indeed discovered stands the test
of time. Cases in point may include the Vitruvian
Man, images of flying machines, and the Mona
Lisa’s androgyny (Boucher, 2003).
A leading voice on information visualization,
Manuel Lima is a Senior UX Design Leader at
Microsoft Bing and founder of VisualComplexity.
com – a visual exploration on mapping complex
networks. His 2011 book entitled Visual Complexity: Mapping Patterns of Information is a
compilation and critical representation of the
processes, ideas and concepts of making data
comprehensibly, logically, and beautifully visual.
Lima’s interests in this line of research began with
his MFA thesis at Parsons School of Design in
2005. His Blogviz project mapped the structure
of popular blog links. Looking at the idea of a
meme (a culture or behavior passed from one
to another), Lima began by charting the citation
frequency of URLs on various blogs. Rather than
represent this information as mere points on a
map, Lima wanted to understand and visualize
the flow/influence/connection that such frequency
suggested. He studied the structure of blogs and
the WWW realizing that the network of blogs is
of a similar structure to most natural and artificial
systems that are represented through diagrams
made of nodes and lines that connect and highlight
relationships between the nodes. As Lima (2011)
explained, “networks are an inherent fabric of life
and a growing object of study in various scientific
domains. . . .This genuine curiosity quickly turned
into a long-lasting obsession over the visual representation of networks, or more appropriately,
network visualization” (p. 15).
Lev Manovich, professor of computer science
at the City University of New York, teaches and
does extensive research on new media theory. Of
late, his work has focused on data visualization and
specifically searchability and findability. Manovich (2012) extends the work of Mark Wattenberg
who visualized such culturally significant data as
a history of net art (Miranda, 2013), Wikipedia
(Viégas & Wattenberg, 2010) and most watched
YouTube videos (Matheson, 2013). With the support of grants from the Digging into Data 2011
Competition, Manovich (2013) and his team
used open source image processing software
designed for use in medical research and other
scientific fields to design a set of tools for media
visualization of large sets of images. Projects
include pages of Science and Popular Science
magazines published 1872-1922, video gameplaying recordings, all paintings by van Gogh,
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What Does Learning Look Like?
and one million Manga pages. Data were gathered
by measuring properties and visual artifacts such
as plotting curves that show patterns of change
and mapping spatial distances according to such
categories as genres, styles, mediums, countries,
and movements. This data can visually organized
through similarities, differences, influence, location, artist, etc. Especially significant to this
chapter is Manovich’s (2008) challenge and value
of new research directions that visualize data in
the social sciences and humanities to understand
“complex social processes like learning, political
and organizational change, and the diffusion of
knowledge” (p. 13).
Art educator Robert Sweeny (2013) looked
to data visualization as art educational research
practice. Through the lens of complexity theory,
Sweeny’s research began with the use of network
structures as interpretive tools and continued
through the question “What does the relationship
between works of visual art and visual culture
look like?” (p. 217). His idea involved the use of
complex visual networks to explore how “works
of art make reference to and link together varied
elements drawn from cultural, social, economic,
religious, and artistic experiences” (p. 222).
Sweeny shared his own visual maps based upon
visual relation, text-based connections, and
between works of art and visual culture. These
maps consisted of thumbnail images of works of
art and visual culture varied in size (according
to level of connection) and arranged in a circular pattern. Zoomed screen shots depicted dots
and lines superimposed on the images to reveal
connections according to medium, text, subject,
geographical location, economic, culture, politics,
and historical references.
UNDERPINNINGS AND
GROUNDWORK
A precursor of this research involving the quest for
visualizing learning could be the 1999 hypertext-
496
based art education study in which high school art
students collected and represented their learning
in interactive computer webs. Virtual boxes were
filled with images of their art work, research, notes,
comments, video, ideas from other classes, music,
and other information linked to their ideas and
learning. Connections were depicted with lines,
arrows, and explanatory notes among boxes and
specific areas like parts of images, phrases and
words and attached explanations of their linking
choices. Lines and arrows depicted the links creating a visual tangled web of learning. Indeed, it
could be said that these interactive computer webs
were visualizations of art learning to a degree (See
Figure 1). The initial research confirmed that the
students who based and connected their study in
these interactive computer webs critically connected what they were learning in class to other
disciplines and realms of experience in and outside
of school. As a result, they were compelled to
learn and know beyond the curriculum content
and guidelines. They were also able to analyze
knowledge and new information through more
connective, comparative, and critically thoughtful ways than they had before the experience
(Taylor, 2000).
This investigation continued for a number of
years through varied class work and collaborations
concerning art criticism, curriculum, artmaking,
and research development (Carpenter & Taylor
2003, 2006, 2009). Most recent work to visualize
learning data through an immersive engagement
with the hypertext involved the beta development
of an electronic learning and assessment tool
for interdisciplinary connections (eLASTIC).
eLASTIC was originally designed to look much
like a computer game (Taylor, 2014). The current
virtual artist studio, laboratory, or gallery spaces
are filled with objects used to house images, text,
sounds, or video clips that can be linked among
other information in the environment. Basically,
instead of pasting an image in a simple virtual box,
in eLASTIC students import images into frames
on the wall; movies are housed in video monitors;

What Does Learning Look Like?
Figure 1. An example from the 1999 hypertext-based high school web of art learning. High school art
students collected and represented their studies by filling virtual boxes with images of their art work,
research, notes, comments, video, other classes, music, and information linked to their ideas and learning. Connections among boxes and specific areas like parts of images, phrases and words were depicted
with lines, and arrows. (© 2014, P. Taylor. Used with permission).
sounds are stored and played through speakers;
and text is placed in various notes. Selected portions of the information placed in these objects
can be linked and the links are depicted as lasers
(color-coded according to student specified paths
such as technique, idea, and/or context). There
is an overhead view that looks much like a map,
an explorer view that mimics a diagram, and
3-dimensional room views (See Figures 2 and 3).
Granted, possible visualizations of art learning
in eLASTIC may include the overviews, screen
shots and interactive searches along with the
simple point-and-click explorations of the student
hypertext constructions. But, what part did the
visual components of the hypertext and eLASTIC
environments play in the students’ learning? The
remainder of this chapter explores the ways that
theory and practice of such data visualization
notables as Lima (2011) and Manovich (2012)
could inform this and the larger research question:
Indeed, what does learning look like?
NETWORK VISUALIZATION
AND DESIGN THINKING
Lima’s (2011) research draws from the graphical
methods classification work done by Williard
Brinton in 1914 and more contemporary notables
such as Ben Fry (2008), Dan Sperber and Deirdre
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What Does Learning Look Like?
Figure 2. In the 2012 beta version of eLASTIC, connections are visible in the room view as laser beams
when the lights are switched off. (© 2014, P. Taylor. Used with permission).
Wilson (1996), Jacques Bertin (1967/2010), and
Bruce Mao (2004). Approaching and creating
network visualizations, according to Lima (2011)
involves roughly eight principles: 1) Start with
a question; 2) Look for relevance; 3) Enable
multivariance; 4) Embrace time; 5) Enrich your
vocabulary; 6) Expose grouping; 7) Maximize
scaling; and 8) Manage intricacy (pp. 82-92). In
a similar vein, Warren Berger (2009) outlined ten
methods/categories of design thinking in his book
CAD Monkeys, Dinosaur Babies and T-Shaped
People: Inside the World of Design Thinking and
How it can Spark Creativity and Innovation: 1)
Ask stupid questions; 2) Jump fences; 3) Make
hope visible; 4) Go deep; 5) Work the metaphor;
498
6) Design what you do; 7) Face consequences; 8)
Embrace constraints; 9) Design for emergence;
and 10) Begin anywhere (pp. 21-267). To further
the idea of representing learning visually, the
remainder of this chapter will correlate Lima’s
data visualization principles and Berger’s design
thinking methods/categories. Examples from
the 1999 hypertext and 2012 eLASTIC research
studies will be offered as illustrative cases, as
well as provocative questions to these theories.
Research methodological approaches for mining
and representing data associated with teaching
and learning in the visual arts will be discussed
and imagined further through descriptions and
visual representations.

What Does Learning Look Like?
Figure 3. The eLASTIC overhead view mimics a map or floor plan of the virtual environment. Lines are
color-coded according to the student’s chosen trail or path such as technique, influence, artist, and or
timeline. (© 2014, P. Taylor. Used with permission).
Questioning Visualizations
Lima’s (2011) first principle “Start with a question” (p. 81) and Berger’s (2009) initial consideration of “Ask stupid questions” (p. 21) obviously point to the importance of the purpose and
meaning of visualization and design. Contrary to
these principles, Manovich (2012) challenged data
visualizers to work “against search” and “look
without knowing what you want to find” (p. 2).
Lamenting hierarchical classification systems,
Manovich challenged researchers to move beyond
postulating “what was there or what are the important types of information worth seeking out.”
In other words, “searching assumes that you want
to find a needle in a haystack. . . .It does not allow
you to see the shape of the haystack. . . .it does
not reveal what else there is worth seeking” (p. 3).
Teaching and learning are highly complex,
individual, and often subjective. Indeed, the visual
representation of teaching and learning would
necessitate many forms. Add the intricacies associated with visual art learning, thinking, making, and
teaching and the possibilities become formidable,
yet inspiringly exciting. Works of art, of course
could be said to be the ultimate data visualization.
Further, as Lev Manovich (2011) wrote:
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What Does Learning Look Like?
Visualizations can also function as art in a different sense: an activity aimed at making statements and asking questions about the world by
selecting parts of it and representing these parts
in particular ways. Some of the most well-known
artistic visualizations projects do exactly that: they
make strong assertions about our world not only
through the choice of visualization techniques,
but also though the choice of data sets (p. 13).
The primary question being addressed—in this
case, what does learning look like?, often determines the way or approach to the visualization
creation. But, visualizing teaching and learning is
a highly charged and controversial endeavor. Other
important issues to consider include: What if there
were ways to represent as well as problematize
visual representations of learning? In other words,
can teaching and learning be visualized critically
to both represent and challenge?
A visualization of the paths a teacher takes
throughout her or his classroom may aid in the
understanding of classroom management issues,
room design restraints to learning, as well as personal bias/value issues that need to be addressed.
When observing student teachers, Dr. B. Stephen
Carpenter uses a technique in which he draws a
quick floor plan of a classroom and then uses a
marker to draw the student teachers’ walking paths.
When the student teacher stops, Carpenter hold
his marker in one place as it literally bleeds into
the paper. This visualization demonstrates the
areas where (and with whom) the student teachers
spend most of their time, as well as the places (or
students) they avoid (personal communication,
April 21, 2001) (See Figure 4).
Some students involved in the eLASTIC
research created maps of some of their paths of
thinking by connecting color-coded links from
sketches and paintings related to specific tech-
Figure 4. Visualizations, such as this map/classroom floor plan provide an annotated reflection of student
teachers’ instructional strategies regarding position and space. (© 2014, P. Taylor. Used with permission).
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What Does Learning Look Like?
niques of artists and writers. Prior to working with
the computer hypertext software, students created
paper-based concept map visualizations on which
they made connections among specific keywords,
ideas, research, and sketches. Indeed, some visual art students’ journal/sketchbooks could be
considered three-dimensional visualizations of
their learning as they are often very inclusive of
a wide variety of inspirational artifacts. Journal/
sketchbooks are typically connectively organized
with tabs or other markers so that young artists can
easily find and share their paths of discovery. In the
original hypertext study, students often presented
their pre-computer work in performative ways. For
example, they physically shared and connected
pages of a journal on a table, books on a shelf,
posters on a bulletin board, notes in a notebook,
and art on an easel by holding hands, using wire,
drawing lines on the floor or simply moving and
swaying between and among the linked objects.
They were performing their own learning data.
Time, Hope, and Consequences of
Visualizing Teaching and Learning
In his chapter “Make Hope Visible” (p. 70)
Berger supported the idea that there is an integral
responsibility of, as well as sustainable stewardship associated with design and innovation. Lima
(2011) explained his principle of “embracing
time” as a means of measuring and mapping to
comprehensively understand the dynamics of
social groups. Such an approach could be linked
to Berger’s (2009) ideas of “facing consequences”
(p. 183) or coming to terms with the responsibility
of design and “embracing constraints” (p. 211)
or repurposing problems. Such issues may urge
the questions: Could visualizing data associated
with teaching and learning do harm to students
and/or teachers? Would it tell things that no one
wants to know and what does that mean? If the
visualization is primarily technology driven, what
measures or considerations should be taken to
insure equal access?
For example, the original hypertext research
actually was dependent upon a specific computer
program that was purchased and only used by
participants for a part of the research project. Consequently, the use and approach ended when the
project, class, and experience was over. Likewise,
the second major part of the research involved the
development of specific software that was again
abandoned by the participants when the project
ended. Many participants felt that using the software was actually disruptive to their learning
because it was yet another separate program or
application from what they already used. They
wanted to be able to gather or mine data from the
technology that they were comfortable with and
wanted to use on their own such as social media.
Some students in the two studies had their own
personal computers and some didn’t. And other
students were able to obtain the software so that
they could work at home. However, not all students
had computers at home.
As exciting and interesting as many of the
student eLASTIC environments were, only a few
of the researchers were actually able to analyze
the intricacies of the links and associations. Not
all of the researchers were familiar with art terms
and techniques. Some of the researchers did not
understand the nuances of the program itself. And,
some of the researchers had difficulty understanding the connections that the students made with
such contemporary youth culture as popular music
and gaming. These issues then beg the questions: Is
it possible for everyone to understand the implications or representations of data visualizations? If
the visualizations are representative of such highly
charged data as student learning, what happens if
they are misinterpreted?
Relevance, Fences, and
Multivariance Associated
with Data Visualization
Lima’s (2011) principle “look for relevancy” (p.
81) and Berger’s (2009) category “jumping fences”
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What Does Learning Look Like?
(p. 45) point to the significance and connection
among the representation, the initial question,
subsequent issues, and audiences. In other words,
who or what else does the design inform, represent, suggest? Does it matter? Is it worth doing
or seeing? Similarly, Lima’s principle “enable
multivariate analysis” suggests and indeed pointedly calls for the value of more than one outcome
variable. “The ties among elements in a network
are immensely rich and detailed, and the inclusion
of additional information can be fundamental in
the unveiling of many of these nuances” (Lima,
2011, p. 81).
Interactive visualizations that offer multiple
levels of information may serve to represent the
numerous layers of teaching and learning. The
point then is to work out a way to capture the various patterns and meanings that we notice when
we directly watch, view, read, and interpret actual
teaching, learning, and artifacts, “as opposed to
metadata about them” (Manovich 2012, p. 2).
According to an interesting word cloud published in Harvard Magazine, the most essential
ingredients of good teaching and learning captured
at a Harvard Initiative for Learning and Teaching
conference included: engagement, listening, active, connections, curiosity, understanding, knowledge, and collaboration. These are represented
as most essential by their large, bold, and bright
font, size and color on the word cloud (Advancing the Science and Art of Teaching, 2013). The
word cloud listed many more ingredients such as
creativity, criticality, focus, interest, ideas, passion, inspire, etc. But, what if visualizations were
designed to capture those first 8 ingredients during
an actual classroom observation? What would it
look like? Could it be like a popup video that was
programmed to record and annotate at the same
time? Or perhaps an observer would have that
ability on a tablet computer in a similar way as
Carpenter’s student teacher observation process
discussed earlier in this chapter. The layers, time,
and pathways of teaching and learning, are but a
few of the many influential aspects that should
502
also come into play when considering the visualization process. How and through what ways does
one come to teach and know? Can someone track
their personal journey of knowing in the visual
arts in a way that others could understand? What
would that look like?
Language, Vocabulary, and
Design of Visualizations
Both Lima (2011) and Sweeny (2013) explain
that much of the vocabulary and design of data
and network visualization is associated with two
primary elements— nodes and links. Varied representations of those nodes and links include such
visual properties as shape, size, texture, intensity,
line variation, etc. Lima also suggested using
expressive edges and layering, but quoted Ben
Fry’s (2008) warning that, “One of the caveats
behind the implementation of diverse graphical
attributes it to beware of creating a visual language that might not be immediately recognized
by everyone” (p. 4). Berger suggested the idea of
designing what you do. In other words, designing
behavior or for the purposes of this research, using
a design approach to not only represent teaching
and learning in a visual way, but design varied
relevant and selective methods for gathering the
data. As stated at the beginning of this chapter,
a painting is not just one painting. Put another
way, one finished work of art does not represent
or make apparent all the valued learning in the
art class. There are multiple indicators/artifacts
of learning that are equally as valuable. So, how
can those indicators be designed so that data is
readily available for visualization?
Specific data was made available in the original
hypertext study (Taylor, 1999) and the eLASTIC
research study (Taylor, 2014) through the use of
templates. For example, required information included names of the artists they studied, personal
and political influences, historical facts, media
and aesthetic theories. Students also needed to
add any of their own personal information that

What Does Learning Look Like?
affected their interpretation of the artist and that
influenced the art that they ultimately created.
Other requirements included links related to media, technique, style and idea. Created primarily
as a guide and starting point for the students, the
template inspired the students to move beyond
the requirements to alter the spaces, questions,
ideas, objects, and links according to their own
interests and trails of thoughts. Looking back
now, the subject, objectives and standards of the
course units were an intricate component of the
template. Therefore, they could easily be related to
data visualization categories/indicators. Interesting too, was the fact that the students seemed to
intuitively use standard-related and data mineable
labels for their links, paths, and boxes (nodes) on
their own. These labels could serve as data points
or sets, as well.
Groups, Metaphors,
and Visualizations
Lima (2011, 2014) drew attention to artistic and
graphical design elements associated with grouping of similarity, spatial proximity, and direction.
Items of similar size, color, and shape typically
depict likeness and/or connection as do objects
that are close to each other or moving in the same
direction at similar speeds (p. 91). Methods/trends
for representing the connections, associations and/
or causes and effects of teaching and learning
may include arc diagrams, flow charts, organic
rhizomes, mapping, and timelines (See Figure 5).
Metaphors may be used to represent an idea,
connection, and/or objects in a visualization. Even
more relevant is Berger’s (2009) suggestion that
the metaphorical experience should be one of
complete immersion. Design should not get in the
way of, but should encourage engagement within
the experience (p. 145). Much like Starbucks
encourages patrons to stay for unlimited hours,
how can the environment, activities and other
contributory indicators of learning be designed
to captivate learning data while at the same time
offer grouping and metaphorical visualization
possibilities?
One way is through visual metaphoric space as
in the virtual gallery space design of the eLASTIC
interface. Interestingly just as Lima (2011) and
Berger (2009) suggest, the students preferred more
structured design elements such as the color-coded
boxes and templates of the original hypertext
software. In retrospect, the students preferred
starting with the template and then altering the
structure of the hypertext as they began seeing and
adding connections and information of their own.
Students in the eLASTIC study couldn’t alter the
virtual environment. They could only change the
information that they placed in the objects. They
could not change or bring in new objects to the
environment. This seems to indicate that active
student engagement in visualizing learning will
probably result in more meaningful data, which in
turn will result in a more accurate and meaningful
visual representation.
Views and Depth of Data
Several factors greatly affect view and interpretation of works of art and data analysis including
vantage point, zooming, panning, and cropping.
Lima (2011) suggested there are three fundamental
views macro (birds eye), relationship (visible inks)
and micro (individual nodes). All three views are
not always necessary in a single data visualization,
but “the more questions it is able to answer, the
more successful it will be” (Lima, 2011, p. 92).
Multiple views of both the hypertext and the
eLASTIC software offer readers and creators
varied ways to approach the information and
structure of connections within the student art
web environments. An overhead or map view
looks much like a concept map with titled lines
and arrows meandering among boxes, objects,
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What Does Learning Look Like?
Figure 5. This overview features five data visualization methods along with their possible interdisciplinary applications in teaching and learning. (© 2014, P. Taylor. Used with permission).
and spaces. Chart, outline, and treemap views
provide a more hierarchical inspection of the file/
portfolio/web/visualization. A map or radial view
mimics a mind or web map of all objects in the
space. (See Figures 6 and 7).
These multiple views reveal connections and
trails of thoughts that are very interesting and
could inform and exemplify teaching and learning. But, going deep and “getting down into the
muck and beauty” (Berger 2009, p. 99) of design,
data, and learning suggest the need for scrutiny
and deep investigation into the purposes or core
business of what is and/or should be going on
in the classroom. What do teachers and students
really need to be successful? What does success
look like and how can it be visualized?
504
A visualization of a students’ thinking trails
as diagrammatic paths in a virtual/metaphoric
environment may represent what is going on in
the art classroom. An even more detailed inspection is possible with the side-by-side comparisons
of connected spaces in the explorer views (See
Figure 7). But, much closer scrutiny is needed to
authentically understand such nuance as; In what
order did the student make these connections?;
How long did it take for the student to come to
this conclusion?; Did a particular author or artist
influence the student to look at his or her work
in a particular way before or after they made the
connection to another artist or author’s technique
or style? Something as simple as adding date and
time stamps attributes to each data entry as well

What Does Learning Look Like?
Figure 6. Multiple visual representations from the original hypertext study include the: chart (top left),
timeline (top right), explorer (bottom left), and treemap views (bottom right). (© 2014, P. Taylor. Used
with permission).
as a software-controlled directional feature may
assist in providing more accurate and meaningful
data for closer scrutiny.
Details, Emergence, and
Patterns of Learning Data
There is a great deal of detail and intricacy to
gathering, analyzing, and visualizing data. Data
sets are typically highly complicated. Lima (2011)
recommended that such complexity of data be
managed through an overview, zoom and filter
to enable users to easily locate specific areas or
patterns of interest (p. 92). Berger (2009) looked
at the intricate details or patterns of everyday life
as designs for emergence and transformation. Key
lessons of such an approach include: 1) design
in a way that is self-sustaining and conducive to
growth; 2) develop community relationship patterns; 3) keep learning; and 4) keep creating and
inventing (p. 247). Important to Berger too, is the
recognition that “small actions are more important
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What Does Learning Look Like?
Figure 7. Multiple visual representations from the 2012 eLASTIC study include Explorer view (Top left
and right) and Radial view (bottom left) shows all linked objects along with a zoom feature (bottom
right). (© 2014, P. Taylor. Used with permission).
than big plans” (p. 267). The following is a comment concerning volunteerism and social justice
by designer Bruce Mau in Berger’s (2009) book.
I use it here to support the idea that visualizing
teaching and learning data can like teaching and
learning, be a form of social action.
Something is happening out there. People are seeing the world around them with greater resolution,
because there is more information available to
them. For the first time, they are seeing patterns
that have been invisible to the larger public in
the past. We are moving toward global clarity—
to people finally being able to say, “Oh, I get it.
Now I see what’s going on in the world” (p. 291).
506
IDEAS AND STRATEGIES FOR
VISUALIZING PATTERNS OF
VISUAL ART LEARNING
The process of making art is filled with rich and
meaningful questioning, researching, risking,
failing, exploring, comparing—in essence the
very principles of data visualization and design
thinking that guided this research. Art curriculum
design is typically begun with a question whether
through backward design, standards of learning,
proficiency levels, works of art or ideas. Vigilant
teachers ask, is this worth knowing or doing and
why? What will students know and be able to do
following a particular learning experience? What

What Does Learning Look Like?
relevance is there in a certain lesson that may serve
to motivate, inspire, encourage, and make a difference? Teachers work toward multiple methods
and indicators of learning. They recognize and
embrace the dynamics of cultural, gender, age, and
social groups. Each class consists of vocabulary
expansion, grouping, comparing, and approaching
art at various levels and scales. Boundaries are
crossed as students are deeply engaged in work that
makes a difference in their world. Learning about
and making art involves design and metaphor. It
requires an understanding of consequences and
constraints of media and ground. And meaningful
art and education requires that artists and teachers
think deeply about how their work may provoke
action, understanding, awareness and/or simple
appreciation through our art and curriculum.
Indeed, if the principles of data visualization
and design thinking are so intricately involved in
art education, then one might assume that they
may in some way determine the patterns associated with a visualized network of art teaching and
learning. What would an art education network
look like? Conversely, Lima (2011) warned,
“Networks are very difficult to visualize and we
do not need to make them more complex in the
process of trying” (p. 95). The ultimate goal is
communicating relevant information. So, what is
the relevant information in art education? University of North Carolina data visualization professor Robert Kosara (2007) wrote that often: “The
goal of artistic visualization is to communicate
a concern rather than to show data” (p. 4). The
major concern of this book is maximizing learning
through knowledge visualization. Another leading
and high stakes issue in education deals with assessment and the ability to reveal concretely the
degree of learning that is going on in visual arts
classrooms. There are other important concerns
as well, that although they extend beyond the
purview of this chapter, their social significance
suggests the need for more than a mere mention.
The value of teaching and learning in the visual
arts; access to quality art education for all students;
funding; student/teacher ratio; classroom space;
the need for cultural and LGBT understanding in
both teaching and subject matter; social justice
in practice and curriculum; and value of and
training for teachers to work with students with
special needs in the art classroom, to name a few.
Although these often highly charged issues may
be viewed as too political and controversial for
the public arena of school, they are very much a
part of our education landscape and ultimately an
integral part of the data visualization. According
to Dörk, Feng, Collins, and Carpendale (2013)
“As visualizations continue to grow in importance,
there is a need to think more systematically about
how values and intentions shape visualization
practice” (p. 2). As such, Dörk, Feng, Collins, and
Carpendale suggest examining hidden intentions
and values, giving full disclosure of data sources,
compelling multiple interpretations/possibilities,
and enabling questioning when approaching data
visualization design.
Similarly, it is important to understand how
visualizations are interpreted and misinterpreted
through social lenses. Cultural coding, visual
metaphor, and symbolic representation as well
as personal and idiosyncratic taste and preference
make visual interpretation highly subjective. Manovich’s (2013) idea that visualizations, like art
can be created in ways that make strong assertions
about the world add another complex layer to the
idea of authentically envisioning schooling. There
is no getting around the fact that the choices made
in the visualization of data in some way affect the
accuracy of that representation.
That said, the following are presented as initial
strategies to be considered when formulating data
visualizations of student art learning.
1. Design the language of teaching and learning (curriculum) with indicators (data) of
learning in mind.
2. Work toward producing multiple and fluid
purposes/functions for visualizations that
can represent as well as challenge.
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What Does Learning Look Like?
3. Remain vigilant in the quest to visualize
needs as well as successes in teaching and
learning.
4. Work toward inclusive and relevant approaches toward data representation acknowledging that there may be more outcome
variables than expected.
5. Consider interactive visualizations for
chronicling multilayered and multidisciplinary learning.
6. Take advantage of similarity, spatial, color,
shape, and directional grouping to assist in
visual identification of likeness.
7. Clearly explain the visual metaphors used to
represent ideas, connections, and/or objects.
8. Provide multiple views and magnification
features to allow for understanding of greater
detail and alternative perspectives.
9. Highlight patterns in recognizable ways
while at the same time honestly conceding
other possible questions for interpretation.
10. Recognize that visualizations are not necessarily universal languages and offer multiple
visual interpretive strategy keys or legends
from which to draw.
box. Another student created multiple clear acetate
sheets that told separate stories by themselves and
built numerous stories as they were layered together. Two other visualizations used the hypertext
software along with Photoshop. Data from their
literature review is housed within the pictured
spaces and they have distorted, transformed,
colorized, layered, and extruded to create effects
that related to their chosen topics of community
arts and online learning (See Figures 8 and 9).
They explain their process:
HOPEFUL RESEARCH VISIONS OF
LEARNING DATA VISUALIZATION
The data visualization unintentionally captured
how what I was learning and how I was learning
was so closely linked to my personal experience.
What began as a casual, yet earnest foray into
community-based art education became a startling
realization about the importance of examining the
day-to-day, “the ritualized inconsequential”, the
familiar and thereby making it strange. The time
of my research coincided with my first semester
of graduate school, a time of personal growth and
great insight into my own learning process. To
represent this, when designing the piece, I used
images of the Blue Ridge Mountains behind my
parents’ home, layering them to create a new
different perspective on the familiar setting from
my childhood (E. Ogier, personal communication,
August 19, 2014).
The future uses, effects, and hopes of data visualization in art and education may inform the actions
of educators as well as portray what is going on,
the needs, what the students are doing, and what
the implications are of goals and practice. In other
words, “a well-designed visualization should have
the unique power to also help shape our identity
as well as our experience” (Lima, 2011, p. 251).
Initial work with graduate students who created data visualizations based on their literature
review explorations revealed some interestingly
varied structures. One student transformed his
pursuit of education theory and practice relating
to students with autism into a jigsaw puzzle game
The data visualization became a visual metaphor
of the links I was making while learning more
about education theorist Jerome Bruner. It started
as a Tinderbox layout with playing blocks in the
background. The further I went into the topic
the more patterns and themes I found. I thought
more about what toys might provide a similar
visual experience and remembered my first time
looking through a kaleidoscope. I felt this fit well
as a kaleidoscope allows you to see a collection
of objects in an also infinite number of patterns
and colors. In the case of this visual the blocks
represented various literature and researchers I
connected to the topic being viewed inside the
508

What Does Learning Look Like?
Figure 8. Graduate student used multiple layers along with screen shots of her hypertext file to add
personal experiences, value of nature and community in her literature review data visualization. A magnified area of the visualization is featured in the upper right corner of the figure. (© 2014, P. Taylor.
Used with permission).
Figure 9. In this visualization, the graduate student added a kaleidoscope pattern to reflect the multifaceted theories of education he was experiencing for the first time. A magnified area of the visualization
is featured in the lower right corner of the figure. (© 2014, P. Taylor. Used with permission).
509

What Does Learning Look Like?
kaleidoscope. This matched my experience up to
that point as I struggled with the frustration of
not being able to present every pattern I found
through out the process. The data visualization is
only one representation among an almost infinite
amount of possible outcomes (M. Kahari, personal
communication, August 21, 2014).
and learning accountability continues to be an
important undertaking for every class including
visual art, such information gathering is extremely
important. The standing of art education will be
greatly bolstered by meaningful and authentic data
visualizations of student art learning.
These first attempts at visualizing learning
consisted of maps or paths of learning using
metaphors of a web, map, pathways, and traces.
The students extended those maps to include more
personal metaphors and designs. Future goals and
considerations include: more student created data
visualizations; multiple format accessibility and
maneuverability; direct links to multiple applications; archiving features; data mining capabilities
across platforms and programs; social networking
and feedback capabilities; visually multidisciplinary; and smart phone and tablet applications
and push notifications. The implications this
research suggests that ongoing and deliberate
data collection and connection serve to maximize
cognitive and creative learning in the visual arts.
ACKNOWLEDGMENT
CONCLUSION
Recognizing and understanding the process of
cognitive learning is crucial to teaching and
schooling. In particular, awareness of the thinking and creative process in visual arts education
is paramount in understanding student growth
and progress. This chapter focused on the ways
that data visualization and design theory and
practice could inform the creation of authentic
and meaningful representations of learning in the
visual arts. Critically capturing and representing
the learning and artmaking processes through
initial hypertext and eLASTIC research was
both presented and challenged. Because teaching
510
The research was made possible by the support
of a National Priorities Research Program grant
from the Qatar National Research Fund and a
research grant from the National Art Education
Association Foundation.
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ADDITIONAL READING
Carpenter, B. S., & Taylor, P. G. (2003). Racing
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Education, 45(1), 40–55.
Carpenter, B. S., & Taylor, P. G. (2009). You’ve
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digital technology. In T. Kidd & I. Chen (Eds.),
Wired for learning: An educator’s guide to web
2.0 (215-232). Charlotte, NC: Information Age
Publishing.
512
Carpenter, B. S., II, & Taylor, P. G. (2006). Making
meaningful connections: Interactive computer hypertext in art education. Computers in the Schools,
23(1/2), 149–161. doi:10.1300/J025v23n01_13
doi:10.1300/J025v23n01_13
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KEY TERMS AND DEFINITIONS
Backward Design: A method of designing
curriculum for education that formulates what
students should know and be able to do before
choosing instructional strategies and evaluation
instruments.
Hypertext: Text, image, parts of images, etc
that is displayed and linked through a computer
or other electronic device to other references. A
reader may click on a hyperlink and flow to the
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connected reference in a hypertext. The World
Wide Web is considered a large hypertext structure.
Multidisciplinary: Refers to being composed
of or combing several disciplines or subjects of
study.
Multivariance: Possessing the multiple possibilities or variables of actions, ideas, or outcomes.
Performative: Unlike performing, performative reflects an act of becoming. One performs an
act that creates a state of affairs such as when an
officiant performs a wedding, two people become
married.
Problematize: To delve more critically into a
situation rather than accepting for face value. To
complicate in order to understand in more detail.
Universal: Of, affecting, or done by all people
or things in the world or in a particular group;
applicable to everyone and everything.
514
ENDNOTES
1
2
The 2-year study involved over 200 students
in Art I-Art IV classes who based all of their
work in computer hypertext webs using
the software Storyspace. They began their
work using a Macintosh SE, then a LC III,
and finally a Powerbook as the study was
completed in 1997. Analyses and dissertation
was defended in 1999. (Taylor, 1999).
This research involved the development and
testing of beta software for a 3-dimensional
version of the earlier hypertextual study.
The research was conducted in Doha, Qatar
from 2009-2012. The research was made
possible by the support of a National Priorities Research Program grant from the Qatar
National Research Fund and a research grant
from the National Art Education Association
Foundation.